Uncertainty-based algorithms can be roughly divided into two main categories, i.e., Bayesian and non-Bayesian approaches. Traditional Bayesian approaches estimate uncertainty by inferring a posterior distribution over the parameters [1]}, [2]}, [3]}. A variety of Bayesian methods have been developed, including Laplace approximation [4]}, Markov Chain Monte Carlo (MCMC) [3]} and variational techniques [6]}, [7]}, [8]}. However, compared with ordinary neural networks, due to the doubling of model parameters and difficulty in convergence, these methods are computationally expensive. Recent algorithm [9]} estimates the uncertainty by introducing dropout [10]} in the testing phase, thereby reducing the computational cost. Several non-Bayesian algorithms have been proposed, including
deep ensemble [11]}, evidential deep learning [12]} and deterministic uncertainty estimate [13]}. Unfortunately, all of these methods focus on estimating the uncertainty on single-view data, despite the fact that fusing multiple views through uncertainty can improve performance and reliability.
